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http://dx.doi.org/10.3745/KIPSTB.2007.14-B.3.215

The Modified ART1 Network using Multiresolution Mergence : Mixed Character Recognition  

Choi, Gyung-Hyun (한양대학교 산업공학과)
Kim, Min-Je (한양대학교 산업공학과)
Publication Information
The KIPS Transactions:PartB / v.14B, no.3, 2007 , pp. 215-222 More about this Journal
Abstract
As Information Technology growing, the character recognition application plays an important role in the ubiquitous environment. In this paper, we propose the Modified ART1 network using Multiresolution Mergence to the problems of the character recognition. The approach is based on the unsupervised neural network and multiresolution. In order to decrease noises and to increase the classification rate of the characters, we propose the multiresolution mergence strategy using both high resolution and low resolution information. Also, to maximize the effect of multiresolution mergence, we use a modified ART1 method with a different similarity measure. Our experimental results show that the classification rate of character is quite increased as well as the performance of the propose algorithm in conjunction with the similarity measure is improved comparing to the conventional ART1 algorithm in this application.
Keywords
ART1; Unsupervised Neural Network; Mixed Character Recognition; Similarity Measure;
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